{"id":"W2095425817","doi":"10.1007/s10858-010-9443-7","title":"Optimizing 19F NMR protein spectroscopy by fractional biosynthetic labeling","year":2010,"lang":"en","type":"article","venue":"Journal of Biomolecular NMR","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"College of Family Physicians of Canada; University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; University of Toronto Mississauga","keywords":"Chemistry; Heteronuclear single quantum coherence spectroscopy; Fluorine-19 NMR; Calmodulin; Nuclear magnetic resonance spectroscopy; Nuclear magnetic resonance; Relaxation (psychology); Phenylalanine; Resolution (logic); Analytical Chemistry (journal); Chromatography; Stereochemistry; Amino acid; Biochemistry; Enzyme","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001076343,0.0009717206,0.0007588078,0.0003883326,0.000561952,0.0009373213,0.0006940676,0.0007328958,0.001313224],"category_scores_gemma":[0.001602346,0.0004349683,0.0003193692,0.00075711,0.0004965118,0.0008394963,0.0006585041,0.0008929145,0.0006837741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001685999,"about_ca_system_score_gemma":0.001314539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001798461,"about_ca_topic_score_gemma":0.003466542,"domain_scores_codex":[0.9994664,0.000106053,0.00002896229,0.000117487,0.0001538566,0.0001273453],"domain_scores_gemma":[0.999522,0.000174618,0.00009523443,0.00006588415,0.00009258782,0.0000497131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003242655,0.00009507947,0.0004596114,0.0000780068,0.00001030463,0.00006361485,0.00005763095,0.005350397,0.9776413,0.002116668,0.0002126151,0.01359052],"study_design_scores_gemma":[0.00002705346,0.0001038916,0.0003054267,0.000004111055,0.00001187847,0.0000699756,0.00002312015,0.01425121,0.9827056,0.0003972805,0.002082706,0.0000177936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8129167,0.0006500582,0.1794045,0.000516544,0.00007370351,0.0002205117,0.0005356282,0.001282119,0.00440014],"genre_scores_gemma":[0.7920844,0.0007229262,0.2042188,0.0001238831,0.00001891481,0.0001738863,0.0005374551,0.0005111003,0.001608602],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001798461,"threshold_uncertainty_score":0.01223278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003507285410891816,"score_gpt":0.2394250291992409,"score_spread":0.2359177437883491,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}